Adaptive Image Sensor Selection for Decodable Indicia Reading
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Solution Overview
Problem
Commercially available decodable indicia reading systems lack the ability to select the appropriate image sensor for effective reading, leading to suboptimal performance in various lighting conditions and spectral content.
Innovation Solution
A decodable indicia reading system that includes a central processing unit and multiple image sensors, allowing for adaptive selection by comparing measured parameters such as decoding time, exposure time, ambient light intensity, or signal-to-noise ratio to a pre-defined threshold, and notifying the operator of the selected sensor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single image sensor is used for decodable indicia reading, then the device complexity is reduced, but the reading performance and signal-to-noise ratio deteriorate under varying lighting conditions
Solution Approach 1:
The system incorporates multiple image sensors with different spectral sensitivities (color and monochrome) within a single data collection device, enabling the system to handle multiple reading conditions and spectral requirements. This multi-functional approach allows the device to adapt to varying lighting conditions and indicia types, improving reading performance without requiring separate dedicated devices for each sensor type.
2Reliability
If multiple image sensors are used without selection capability, then the signal-to-noise ratio can be improved under specific conditions, but the device complexity increases
Solution Approach 1:
The system employs a feedback mechanism where the CPU continuously monitors reading conditions (such as ambient light levels, indicia characteristics, and preliminary decode attempts) and uses this information to dynamically select the most appropriate image sensor. This feedback-driven selection process ensures that the system automatically chooses the optimal sensor for current conditions, improving signal-to-noise ratio and reading success rates without requiring complex manual intervention or pre-configuration by the operator.
3Reliability
If adaptive sensor selection is implemented, then the decode success rate improves under varying conditions, but the processing time and system complexity increase
Solution Approach 1:
The system performs preliminary assessments of reading conditions and sensor suitability before initiating the actual decoding operation. By evaluating ambient light levels, indicia characteristics, and sensor capabilities in advance, the system pre-determines the optimal sensor selection, thereby minimizing the time required during the actual reading process. This preliminary action ensures that the decode success rate is maximized while keeping the time penalty for sensor selection minimal.
Data Source
AI summary
A decodable indicia reading system can be provided for use in locating and decoding a bar code symbol represented within a frame of image data. The system can comprise a central processing unit (CPU), a memory communicatively coupled to the CPU, and two or more image sensors communicatively coupled to the CPU or to the memory. The system can be configured to select an image sensor for indicia reading by cycling through available image sensors to detect an image sensor suitable for an attempted indicia reading operation by comparing a measured parameter value to a pre-defined sensor-specific threshold value. The system can be further configured to select the first suitable or the best suitable image sensor for the attempted decodable indicia reading operation based upon the comparison result. The system can be further configured to notify the system operator which image sensor has been selected. The system can be further configured to obtain a decodable indicia image by the selected image sensor.